Deepgram says speech recognition still fails on real-world latency
The article from Agora features Andrew Seagraves of Deepgram discussing the current state of speech recognition technology. It highlights that while optimized for narrow cases, speech recognition still has significant gaps in real-world applications related to data and latency. Deepgram focuses on addressing these challenges in the voice AI domain.
Key Takeaways
- Andrew Seagraves says speech recognition is optimized for narrow cases, not real-world use.
- Deepgram points to two main gaps: data coverage and latency.
- The discussion appears in Agora’s blog, tying the issue to voice AI for video applications.
Why It Matters
The immediate takeaway is that speech recognition quality is still uneven once products leave controlled demos and enter real-world workflows. That matters for streaming and video applications because latency and data gaps directly affect voice AI experiences. The competitive angle is less about a finished category and more about which vendors can handle messy, real-world speech inside production systems. Watch for whether Deepgram and Agora keep emphasizing latency and data coverage in future product or developer materials.
Read full article at prod.agora.io